A classical approach to abnormal activity detection is to learn a
representation for normal activities from the training data and then use this
learned representation to detect abnormal activities while testing. Typically,
the methods based on this approach operate at a fixed timescale - either a
single time-instant (eg. frame-based) or a constant time duration (eg.
video-clip based). But human abnormal activities can take place at different
timescales. For example, jumping is a short term anomaly and loitering is a
long term anomaly in a surveillance scenario. A single and pre-defined
timescale is not enough to capture the wide range of anomalies occurring with
different time duration. In this paper, we propose a multi-timescale model to
capture the temporal dynamics at different timescales. In particular, the
proposed model makes future and past predictions at different timescales for a
given input pose trajectory. The model is multi-layered where intermediate
layers are responsible to generate predictions corresponding to different
timescales. These predictions are combined to detect abnormal activities. In
addition, we also introduce an abnormal activity data-set for research use that
contains 4,83,566 annotated frames. Data-set will be made available at
this https URL Our
experiments show that the proposed model can capture the anomalies of different
time duration and outperforms existing methods.

提出了一种多时间尺度模型，以在不同时间尺度上捕获异常活动，并通过对不同时间尺度的输入姿势轨迹进行未来和过去预测来检测异常活动。